The core idea is that, when understanding, explaining and/or predicting the behavior of an object, we can choose to view it at varying levels of abstraction. The more concrete the level, the more accurate in principle our predictions are; the more abstract, the greater the computational power we gain by zooming out and skipping over the irrelevant details. Dennett defines three levels of abstraction, attained by adopting one of three entirely different "stances", or intellectual strategies: the physical stance; the design stance; and the intentional stance: The most concrete is the physical stance, the domain of physics and chemistry, which makes predictions from knowledge of the physical constitution of the system and the physical laws that govern its operation; and thus, given a particular set of physical laws and initial conditions, and a particular configuration, a specific future state is predicted (this could also be called the "structure stance").[15] At this level, we are concerned with such things as mass, energy, velocity, and chemical composition. When we predict where a ball is going to land based on its current trajectory, we are taking the physical stance.

DLA Piper will use artificial intelligence technology by Kira Systems for due-diligence document review in mergers and acquisitions. Kira's machine-learning software searches texts in contracts, then creates a summary and analysis, according to a press release announcing the deal. Such software is designed to get smarter as it is used. Jonathan Klein, chair of DLA Piper's U.S. mergers and acquisition practice, said in the press release that the technology will make due diligence faster and more efficient, and will mitigate risk throughout the due diligence practice. "We believe that this innovative technology will do for corporate transactional work what e-discovery has done for litigation," he said. DLA Piper already tried the software in its corporate, intellectual property and technology practices.

IntelligentX Brewing Co. has introduced what it calls "the world's first beer brewed by artificial intelligence." The London-based microbrewery is putting a new twist on an old tradition, using machine learning and a Facebook Messenger bot to spot trends and improve its product. "We currently live in a world where companies are using our data to target more advertising at us. We believe we can do something more useful with data," IntelligentX co-founder Hew Leith said in a statement to PCMag. "We can use data to create better products that improve over time. "In this example we [are] combining customer feedback data and a machine-learning algorithm to determine what consumers like about our beers, then brew new versions which are more finely tuned to people's tastes," he continued. "This is the future of data.

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The goal is to find a way to consistently produce data-driven insights at enterprise scale. This can be done with industrial machine learning (IML), which provides a scalable solution for ingesting data, building algorithms, deploying them into production, and generating continuous insights to ongoing business problems. IML is a modern take on a very old idea: the scientific method. Data scientists start with a hypothesis and collect data that could be useful in evaluating the hypothesis. They then generate a model and use it to explain the data.

Bain Capital Ventures announced today in a public filing that it has closed a 600 million investment fund. BCV, the venture arm for Boston-based firm Bain Capital, said it "also has money remaining in its [ 200 million] 2014 Coinvestment Fund for larger investments." The new fund, the firm said, will continue with previous BCV investing trends, pouring seed and growth capital into enterprise and infrastructure software startups, as well as in "industries transformed by data." BCV managing director Ajay Agarwal tells VentureBeat that specific areas of focus for the new fund include "SaaS, machine learning, financial and retail technology, cybersecurity, cloud infrastructure and open source." "We will also continue to balance investments geographically, including California which now represents half of our senior investment team and half of our investment dollars," said Agarwal.

The Air Force wants white papers that describe new ways quantum computing could help achieve its mission, according to an amended Broad Agency Announcement posted Friday. Eventually, the government could provide a test-bed where a contractor might install, develop and test a quantum computing system, according to the announcement. Last year, the Air Force announced it had about 40 million available to fund research into, and the eventual maintenance and installation of a quantum system -- a branch of emerging computing technology that relies on the mechanics of atomic particles to process complex equations. The Air Force Research Laboratory's Information Directorate, which focuses on processes such as signal processing, networking technology, cyber research and supercomputing, is collecting those white papers. Many problems that stump traditional computers can be re-framed as "optimization problems," the solicitation says; one of quantum computing's potential benefits is its ability to quickly find the optimum solution to a multidimensional problem, given certain constraints.

The three cutting-edge frameworks showcased in these talks from MesosCon North America demonstrate the amazing power and flexibility of Apache Mesos for solving large-scale problems. Perhaps you have noticed, in our Apache Mesos series, the importance of frameworks. Mesos frameworks are the essential glue that make everything work in a Mesos cluster, the layer between Mesos and your applications. They perform a multitude of tasks, including launching and scaling applications, monitoring and health checks, configuration management, and scheduling. In these talks, you'll learn how: Netflix uses Mesos to power their recommendation engines.

There's a lot of hype in the world at the moment about how algorithms are taking over the world. There's a lot of hype in the world at the moment about how machine learning is taking over the world. "Machine learning" is one of those things that is terribly complicated to the extent that most people don't really understand it. I've been reading a book recently, The Master Algorithm, which seems to make a pretty good stab at explaining both Algorithms and Machine Learning. I've no doubt that machine learning and algorithms will continue to change the world around us.

Cisco researchers have managed to spot malware in encrypted traffic, making a new development in the fight against malware. A group of Cisco researchers have managed to sport malicious traffic in encrypted traffic without any need to decrypt data. The discovery could pave the way for products that can secure networks while maintaining privacy. According to a paper published on Arxiv, malware within encrypted streams gives out enough clues to allow researchers to spot them. Traffic encrypted using TLS, is increasingly used by criminals to circumvent security products. "The use of TLS by malware poses new challenges to network threat detection because traditional pattern-matching techniques can no longer be applied to its messages," Blake Anderson, Subharthi Paul, and David McGrew said in their research paper.